Assessment of the need for DCE MRI in the detection of dominant lesions in the whole gland: Correlation between histology and MRI of prostate cancer
Bibliographic record
Abstract
The purpose of this study was to evaluate the utility of dynamic contrast-enhanced magnetic resonance imaging (DCE MRI) in the detection of dominant prostate tumors with multi-parametric MRI of the whole gland. Combined diffusion tensor imaging (DTI) and DCE MRI from 16 patients with biopsy-proven prostate cancer and no previous treatment were acquired with a 3.0-T MRI scanner prior to radical prostatectomy, and used to identify dominant tumors. MRI results were validated by whole-mount histology. Paired t-test and Wilcoxon test, logistic generalized linear mixed effect models and receiver operating characteristic (ROC) analyses were used for the estimation of the statistical significance of the results. In the peripheral zone (PZ), the areas under the ROC curve (ROC-AUC) were 0.98 (sensitivity, 96%; specificity, 98%) for DTI, 0.96 (sensitivity, 92%; specificity, 97%) for DCE and 0.99 (sensitivity, 98%; specificity, 98%) for DTI + DCE. In the entire prostate, the ROC-AUC values were 0.96 (sensitivity, 84%; specificity, 95%) for DTI, 0.87 (sensitivity, 45%; specificity, 94%) for DCE and 0.96 (sensitivity, 88%; specificity, 98%) for DTI + DCE. The increase in ROC-AUC by the addition of DCE was not statistically significant in either PZ or the entire prostate. The results of this study have shown that DTI identified dominant tumors with high accuracy in both PZ and the entire prostate, whereas the inclusion of DCE MRI had no significant impact on the identification of either PZ or entire prostate dominant lesions. Our results suggest that the inclusion of DCE MRI may not increase the accuracy of dominant lesion detection, allowing for faster, better tolerated imaging studies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".